kingchenc
8431b1400c
B12 of the family-deepening roadmap — seven Tom DeMark indicators (467 -> 474).
**Candle -> +1/0 qualifier patterns (candlestick macro bindings):**
- **TD Camouflage** — hidden intrabar strength/weakness against the prior close.
- **TD Clop** — two-bar open/close engulfing reversal.
- **TD Clopwin** — the inside-body cousin of TD Clop (compression bar).
- **TD Propulsion** — continuation thrust closing beyond the prior extreme.
- **TD Trap** — inside ("trap") bar followed by a range breakout.
**Hand-bound:**
- **TD D-Wave** — streaming Elliott-style 1-5 / A-C swing-wave counter (candle -> f64, `strength` param).
- **TD Moving Averages** — ST1/ST2 median-price trend ribbon (candle -> struct {st1, st2}).
All seven join the existing **DeMark** family. Patterns follow the house-style
+1/0 candle-pattern convention (neutral 0.0 during warmup). Public binding names
use the family-consistent `TD...` casing.
Wiring complete across core, Python, Node, WASM, fuzz, tests, README + docs
counter (474) and CHANGELOG. Verified: core 3874 + doc 427, clippy clean,
node 549, python 903.
Wickra — Python
Streaming-first technical indicators for Python. pip install wickra — no
system dependencies, no C build tooling.
Wickra is a multi-language technical-analysis library with a Rust core and bindings for Python, Node.js, and WebAssembly. Every indicator is an O(1) streaming state machine, so live trading bots and historical backtests share the exact same implementation. This package is the Python binding (PyO3); it exposes 200+ streaming-first indicators across sixteen families.
Install
pip install wickra
Pre-built wheels ship for Linux, macOS, and Windows — there is nothing to compile and no C library to track down.
Quick start
import numpy as np
import wickra as ta
# Batch: classic TA-Lib-style usage over a whole array.
prices = np.linspace(100, 200, 1000)
rsi = ta.RSI(14)
values = rsi.batch(prices) # numpy array, NaN during warmup
# Streaming: the same indicator, fed tick by tick in O(1).
rsi = ta.RSI(14)
for price in live_feed:
value = rsi.update(price) # no recomputation over history
if value is not None and value > 70:
print("overbought")
batch(prices) and feeding the same prices through update() produce
identical values — the equivalence is enforced by the test suite.
Documentation
The full indicator catalogue, guides, quickstarts, and API reference live in the main repository and documentation site:
- Repository & full indicator list: https://github.com/wickra-lib/wickra
- Docs (quickstarts, cookbook, TA-Lib migration): https://docs.wickra.org
- Runnable examples:
examples/python/
Wickra ships four bindings — Python, Node.js, WebAssembly, and Rust — that all
expose the same indicators from the shared, unsafe-forbidden Rust core.
Disclaimer
Wickra is an indicator toolkit, not a trading system. The values it computes are deterministic transforms of the input data — they are not financial advice and do not predict the market. Any use in a live trading context is at your own risk. The library is provided as is, without warranty of any kind.
License
Licensed under either of Apache-2.0 or MIT at your option.